Pattern recognition using neural networks: theory and algorithms for engineers and scientists
Pattern recognition using neural networks: theory and algorithms for engineers and scientists
Subspace based feature selection for pattern recognition
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Cross-fuzzy entropy: A new method to test pattern synchrony of bivariate time series
Information Sciences: an International Journal
Wavelet entropy and neural network for text-independent speaker identification
Engineering Applications of Artificial Intelligence
Wavelet speech feature extraction using mean best basis algorithm
NOLISP'09 Proceedings of the 2009 international conference on Advances in Nonlinear Speech Processing
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
International Journal of Speech Technology
Information Sciences: an International Journal
Hi-index | 0.00 |
This paper proposes the use of the discrete wavelet transform (DWT) for the extraction of features from phonemes. Instead of using the short time Fourier transform for feature extraction a new set of features is obtained from the DWT. The new set of features overcomes the previously reported problem of shift variance in DWT based features. Training and test samples of the phonemes were obtained from the TIMIT database. To account for the fast changes in the phonemes, the features were calculated for different phoneme durations and the performance was compared. For the classification of the phonemes, two different classifiers were used, based on linear discriminant analysis and multi-layer perceptron.